Wealth and inequality gradients for the detection and control of hypertension in older individuals in middle-income economies around 2007-2015

Socioeconomic inequalities in the detection and treatment of non-communicable diseases represent a challenge for healthcare systems in middle-income countries (MICs) in the context of population ageing. This challenge is particularly pressing regarding hypertension due to its increasing prevalence among older individuals in MICs, especially among those with lower socioeconomic status (SES). Using comparative data for China, Colombia, Ghana, India, Mexico, Russia and South Africa, we systematically assess the association between SES, measured in the form of a wealth index, and hypertension detection and control around the years 2007-15. Furthermore, we determine what observable factors, such as socio-demographic and health characteristics, explain existing SES-related inequalities in hypertension detection and control using a Blinder-Oaxaca decomposition. Results show that the prevalence of undetected hypertension is significantly associated with lower SES. For uncontrolled hypertension, there is evidence of a significant gradient in three of the six countries at the time the data were collected. Differences between rural and urban areas as well as lower and higher educated individuals account for the largest proportion of SES-inequalities in hypertension detection and control at the time. Improved access to primary healthcare in MICs since then may have contributed to a reduction in health inequalities in detection and treatment of hypertension. However, whether this indeed has been the case remains to be investigated.

sure it is accurate.  The authors have declared that no competing interests exist. NO    Yes -all data are fully available without restriction The burden of non-communicable diseases (NCDs) in middle-income countries (MICs) is 2 growing due to demographic and lifestyle changes. Already 85% of all premature NCDs 3 deaths are in MICs (1). This is a pressing concern for low-resourced economies which 4 have yet to deal with transmissible diseases as well as the lack of adequate human 5 capital and physical resources of their healthcare systems (2), therefore threatening to 6 achieve the Sustainable Development Goal of universal healthcare coverage that most 7 countries are committed to (3). One condition that summarises this threat is 8 hypertension or high blood pressure (HBP), a condition which affects 22% of the 9 population aged 18 years or older (1) and about 60% of individuals aged 60 years or 10 older worldwide (4). Yet, it is preventable and treatable, making it a specifically useful 11 measure of healthcare system effectiveness (5).

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Besides universal coverage, another critical objective of healthcare systems is to 13 improve health equity, thus reducing inequalities in health according to socioeconomic 14 October 1, 2021 1/19 Manuscript Click here to access/download;Manuscript;Ageing_HBP_Control_LMIC.pdf status (SES), and in particular to wealth (6). There exist significant health disparities 15 according to SES in most MICs, with an epidemiological transition occurring during 16 which the disease burden of NCDs is shifting from individuals with higher SES to 17 individuals with lower SES (7). Inequalities in access are often associated with the 18 source of health insurance funding, partly driven by the large informal sector of the 19 economy in MICs. 20 Although the successful prevention and treatment of NCDs among individuals from 21 all socio-economic backgrounds present a litmus test for health systems in MICs (8), 22 evidence on inequalities in NCDs in MICs and the potential effectiveness of healthcare 23 systems in reducing the latter has at least two crucial shortcomings. First, few studies 24 use objectively measured prevalence of NCD in general and HBP in particular, mostly 25 relying on self-reported prevalence. This is problematic for two reasons. On the one    medical and health conditions, as well as use and access to health services.

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For Colombia, the Healthcare, Welfare and Ageing Survey (SABE, for its acronym in 72 Spanish) was collected in 2015, and its design is comparable to the other ageing 73 population surveys (23). Appendix A presents the main characteristics of the health 74 systems of these countries.

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One of the main characteristics of those surveys among older individuals is that they 76 involve clinical measures such as blood pressure. Following a standardised procedure, 77 nurses measured three times systolic and diastolic BP. After discarding the first BP 78 measurement, we take as our objective measure of HBP whether the average of the 79 measurements is above 140 mmHg for systolic BP, or above 90 mmHg for diastolic BP. 2 80 We include in our analysis all observations of individuals aged 60 and older for 81 whom there is available information in the following characteristics: 3 valid 82 measurements of blood pressure, self-reported diagnosis of high blood pressure, gender, 83 age, education, weight and height, smoking behaviour, assets, and health insurance (in 84 all countries but Mexico). As a common limitation for using biomarkers data, the 85 resulting sample is likely to be more educated, wealthier and care more for their health 86 than the regular population (24; 25). 4 Table 1  objectively measured BP levels that indicate otherwise. (ii) Undetected HBP: those 96 respondents who report not having HBP, but according to their BP measurements, they 97 diagnosed with HBP, but the BP measurements suggest that current treatment (if any) 99 is ineffective. And (iv) Controlled HBP: those respondents who are aware of HBP, 100 and whose BP levels are under control.

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In this study, the denominator of our analysis are individuals who either report 102 having ever been with HBP, or those whose BP is above the standard diagnosis 103 threshold (groups ii to iv). Henceforth, we refer to them as the at-risk population 104 (AR). While group (i) is essential for public health interventions to prevent them from 105 developing HBP, in this work, we concentrate on those who already require attention by 106 the health system. In this sub-population, it is possible to discuss the false negatives of 107 the system (group ii). 6 The other leading group of interest is (iii), as it involves those 108 for whom the health system has not provided effective treatment. Ideally, these two 109 groups should be zero. Another element of interest is the proportion of those who are 110 aware (groups iii and iv) and are receiving treatment. We concentrate on an outcomes 111 perspective, rather than on an input perspective. Therefore we make emphasis in the 112 controlled/uncontrolled partition. Thus, our main dependent variables will be:

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• to have undetected HBP for those individuals in the population AR;

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• to have uncontrolled HBP conditional on being aware of the HBP condition.  In order to estimate the relationship between undetected and uncontrolled HBP with 132 household wealth, we estimate logistic regression models. The probability of observing 133 that for individual i outcome Y i take the value of 1 instead of 0, conditional on a set of 134 covariates X i , country R i , and wealth index wealth i is given by the following equation: 135 where Λ represents the logistic function. For the case of uncontrolled HBP, the 136 sample is restricted to those diagnosed with the condition. In this regression, on top of 137 the countries fixed effects and a dummy for respondents being surveyed in 2009/10, 7 we 138 include the interaction between the country dummies and the independent variables. As 139 a result, the interaction of the wealth index and the country dummy provides a measure 140 of the gradient (γ r ) for each country. These models were estimated using Stata 16.  This decomposition technique was used to study mean outcome differences in 154 uncontrolled and undetected High blood pressure (HBP) by poverty status. We 155 considered as poor to 20% of people with the lower income. 8 This method divides the 156 uncontrolled or undetected HBP differential between two groups (poor and non-poor) 157 into a part that could be explained by group observable characteristic differences and a 158 residual part that cannot be accounted for by such differences in HBP determinants.

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The unexplained part subsumes the effects of group differences in unobserved predictors. 160 We used a twofold decomposition using the coefficients from a pooled linear 161 probability model over both groups as the reference coefficients. Then, the outcome 162 difference is divided into two components: where the first component, is the part of the outcome differential that is explained by group differences in the 165 predictors. As predictors, we used behavioural risks which include the variables of age 166 and being male, demographic risks, which have smoking history and obesity status, and 167 a variable that indicates if the individual lives in an urban area. The second component 168 in this decomposition, is the unexplained part, which in addition to capture all the potential effects of    Table 1 presents as well socio-economic characteristics of the respondents. Age and 208 gender produce 'mechanical' differences between countries. However, Russia has fewer 209 men in the sample (33%), yet BP levels are one of the highest; the South African 210 population is on average as old as the Indian (68 years ), but there are more than ten 211 mmHg of difference between their BP levels. Differences are more likely to be related to 212 habits and lifestyle: obesity in South Africa is nearly 50%, while in India it is 4%. Yet, 213 China obesity rate is as well low, 7%, but their average systolic BP is the same in South 214 Africa. Mexico and Colombia have the highest rate of people living in an urban area, 215 close to 80%. However, BP in Mexico is higher than in Colombia. Although Mexico has 216 the highest wealth index also has one of the highest BP in our sample of countries.

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Another consideration is that there are no substantial systematic differences between 218 the BPS and AR population because AR represents a large proportion of all individuals 219 older than 60 in these countries.

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Another set of variables presented in Table 1 is related to the health system. As for living in rural areas, where more barriers have been found to expand universal 227 coverage in the world. Unfortunately, this information is not available for Mexico.

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Socio-economic characteristics, lifestyle, and health system differences shape the 229 relationship between wealth and health in each country. The unconditional relationship 230 between wealth and our HBP quality of attention indicators are presented in Figure 2. 231 Each point of the line presents the average rate of undetected and uncontrolled HBP for 232 each level of wealth. First, it shows a negative slope between wealth and probability of 233 non-detection of HBP, which depends on the country. Second, concerning uncontrolled 234 HBP, there are both positive and negative slopes, showing that the role of wealth is 235 context-dependent. With the AME derived from the parametric model presented above, 236 we can determine whether the observed slopes hold after conditioning of different sets of 237 controls.  Table 2 presents the AME after logistic regressions. It considers three models for each 240 outcome which differ on the set of controls. Columns (1) and (5) only consider country 241 fixed effects, age, and a gender dummy. Columns (2) and (6)  Russia. South Africa is on the opposite scenario, where the gradient is around 2.05 pp 256 without education and 1.66 pp (not significant at 90% level) when these controls are 257 added. Health system variables reduce the gradient slightly in most countries, especially 258 in China. The considerable reduction on the gradient for Russia has to be taken 259 carefully, as less than 1% of the individuals at risk has VHI, and none has no insurance. 260 Therefore the change is such point estimate obeys more to the high imprecision of the 261 estimate (a 95% confidence interval between -6.28 pp and -83.11 pp).

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Regarding uncontrolled HBP, table 2 presents a similar analysis but conditional on 263 the diagnosis. Here we are exploring the probability of having HBP in comparison with 264 a mix between those people with controlled HBP and those who are not aware of the 265 condition. 9 The table shows a significant negative gradient for all countries but for 266 Mexico and South Africa. For these countries, the gradient is negative as well, but the 267 precision of the estimates cannot rule out the non-presence of an association. The 268 largest figure is for Russia (-0.41 in column 6) and Mexico (-0.42, but not significant at 269 95% level), and the smallest for South Africa (-0.11). Notice that the dispersion 270 between countries is smaller than for detection. In most cases, the addition of health  for systolic blood pressure and an alternative multinomial logistic model. Regardless of 276 the model specification, and even with the continuous systolic BP variable, similar 277 patterns to the ones presented above are obtained. This is also the case when alternative 278 definitions of the age range (appendix D) and the assets index (appendix E); and when 279 individual regressions per country and set of controls are considered (appendix H).

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We further explore the gradient by comparing differences between those at the bottom 282 of the wealth distribution (the lowest quintile) with the rest of the sample. The black 283 diamonds in Figure 3 present the difference in the proportion of undetected HBP (on the 284 left) and uncontrolled HBP (on the right). Concerning the previous section, the pattern 285 for the overall gap differs in the case of Russia. For this country, there is a small gap in 286 this exercise while a large wealth gradient. This indicates that the gradient is probably 287 linked to a specific upper interval of the domain of the wealth index (where most of the 288 distribution is located as shown in Figure 1) Almost all the explained differences are due to the urban/rural division in the first 303 place and the education level in the second. Again, for the case of China, differences in 304 the proportion of people living in urban areas represent 80.15%(=-0.105/-0.131) of the 305 explained difference; this figure is also high for South Africa (83.5%), and very small in 306 Mexico (3.4%). Differences in education level represent another 11.8% in China, and 307 just an 8% for the rest of the variables (socio-demographics, smoking prevalence and 308 obesity). In Mexico, 91.3% of all the explained gap can be associated with education. In 309 general, these two factors account for most of the observed gap (more than 80% of it). 310 However, in Ghana, age and gender differences represent nearly 11.3% of the explained 311 difference. In Figure C1 in the appendix, we perform the decomposition separately by 312 urban and rural areas. It shows that all the other observed characteristics play a minor 313 role in explaining wealth gaps within these areas.  These variables explain 14% of the difference in Ghana, 11.6% in South Africa, 10.6% in 317 India, 9.4% in Colombia, and 2.5% in Russia. In contrast, for China (-3.5%) the level of 318 observable variables suggest that the gap should run in the opposite direction.

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The absolute difference in uncontrolled HBP, conditional on the diagnosis, between 320 poor and non-poor people is statistically significant for China, Colombia and South 321 Africa (see Table C3 in the appendix). For the other countries, the total difference 322 cannot be rejected to be equal to zero. For Ghana, India, Mexico, and Russia, there is 323 no evidence of a gap though there was evidence of a gradient. Once again, this might be 324 related to the point of comparison of the wealth distribution. Ghana presents interesting 325 results, as the point estimate is small, but the explained component suggests a gap 326 similar to the Colombian or the Indian. For this country, any analysis should be careful 327 as the number of individuals diagnosed with HBP is small, so results are very imprecise. 328 For the three countries for which there is evidence of a gap, the explained component 329 is roughly between 50% and 60%. For China and Colombia, more than 90% is explained 330 by rurality, while this factor is only 36% in South Africa. In the last country, education 331 accounts for 77% of the explained gap, while it is 6% in China and Colombia. When 332 health insurance variables are included (Table C5 in the appendix), there are almost no 333 changes at all, and these variables explain around 4% to 6% of the explained gap.

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As discussed in the methods section, the BO decomposition with linear probability 335 models might be biased for covariates with a strong non-linear relationship with the 336 outcome. In the appendix, tables C6 and C7 show the results of (27) extension based on 337 a logit model. Results are almost the same as those presented above. Moreover, as for 338 the results of the marginal effects, we consider alternative definitions of the age range 339 (appendix D), the assets index (appendix E), and the uncontrolled HBP results 340 conditional on being aware of the conditions (appendix E). We also consider the 341 sensitivity of the results to alternative definitions of the non-poor sample (appendix G). 342 Qualitative results are stable across these alternatives. 343

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In this study, we explored the role of household wealth on the quality of seven MICs' 345 health systems for attending mature patients aged 60 and older with HBP, both aware 346 and not of their condition. This condition, which requires an effective action from 347 several layers on the primary health care system, is central for preventing the onset of 348 cardiovascular diseases.

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As presented by (29), treating HBP has four inter-related goals: awareness, 350 treatment, antihypertensive medication adherence and control. Despite differences in 351 health systems, there exist several common elements for attaining these goals: access to 352 health insurance, low prices/co-payments for medication, consistent access to health 353 care. Over these areas, the MICs analysed in this study present important differences: 354 • The population at risk is between 67% and 81% in China, Colombia, Mexico, 355 Russia and South Africa, compared to the range of 39% -57% in Ghana and India. 356 This is potentially linked with the higher urbanisation rates of the first countries. 357 Obesity also might play an essential role for Colombia, Mexico and Russia.

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• Russia and Colombia have reasonable detection procedures, which allow them to 359 have high awareness rates (75%-82%). Ghana does a poor job with 26%, and in 360 other countries included in this study, only half of those at risk are aware of their 361 condition.

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An important indicator of health system effectiveness is the degree to which it 363 reduces wealth-related inequalities in health. As the results of this study show, in all of 364 the MICs included, there are significant wealth-related differences in the likelihood of 365 having detected HBP, with individuals with lower wealth being less likely to be 366 diagnosed than those with higher wealth. To explore the roots of these gradients, we 367 considered the following factors: demographic (age and gender), behavioural (obesity 368 and smoking), socio-economic (education and living in urban areas), and health system 369 (access to compulsory and voluntary health insurance). We found that: • Wealth is essential in all countries for determining awareness, but it is especially 371 relevant in Ghana and Russia. In all countries, the wealth gradient is less 372 pronounced once we control for access to HI.

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• Once diagnosed with HBP, the wealthier the individuals, the more likely their BP 374 is under control. For China, the gradient disappears once health insurance is 375 under control.

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• The most relevant observed factor for the gap on undetected and uncontrolled 377 rates is to live in a rural area rather than an urban area. It is substantially more 378 important for most countries than education level, the second determinant in the 379 list. Differences in demographic characteristics and behavioural risks play a small 380 role in explaining large differences in most countries. This is likely because the 381 wealth gap on these variables is relatively small, rather than that these variables 382 are not associated with the outcomes. For two countries, we can present a comparison with the literature. First, for China, 396 the gradient in prevalence is non-existent, and that there is a positive gradient between 397 detection and control (18). Second, for South Africa, results are in line with (11) who 398 found no socio-economic gradient in unawareness once controls are considered. They use 399 a large sample that involves respondents of all ages and estimate the gradients using a 400 finite-mixture model that accommodates unobserved heterogeneity. Another difference is the period: Russia surveys are from 2007 to 2010, while the Colombian one from 2015. However, our comparison is between the state of two health systems based on control of HBP. There are no many differences in terms of the technology available or their prices between these two periods. Notes: Epanechnikov Kernel densities using a bandwidth of 0.03 (asset index ranges from 0 to 1). Graph constructed over the sample of SABE and SAGE of respondents with a valid BP measure and a self-report of HBP. The asset index was derived using factor analysis for all individuals who answered the assets section. See online appendix B.2 for further details.